The efficiencies defence in merger cases: implications of alternative standards
Bibliographic record
Abstract
We compare alternative interpretations of the efficiencies defence, provided under Canadian competition law, for mergers found likely to lessen competition substantially. We find the respective percentage reductions in long‐run marginal cost required for a profitable merger to satisfy the total surplus, price, and two weighted surplus standards, given pre‐merger market structure. We find that when efficiency spillovers are low and markets are concentrated, the cost reduction required to satisfy the price standard is over four times higher than is required for a profitable, total‐surplus‐increasing merger and the cost reductions required to satisfy the weighted surplus standards are nearly twice as high. Les auteurs comparent diverses interprétations des stratégies de défense en terme d'efficacité pour les fusions dont on pense qu'elles vont réduire substantiellement le concurrence dans le cadre de la loi sur la concurrence au Canada. Ils identifient que les réductions en pourcentage dans le coût marginal de longue période qui sont requises pour qu'une fusion profitable satisfasse aux normes du surplus total, du prix, et des deux surplus pondérés, compte tenu de la structure de marché avant la fusion. Il semble que quand les effets de débordement d'efficacité sont faibles et que les marchés sont concentrés, les réductions de coûts requises pour satisfaire la norme de prix soient quatre fois plus élevées que ce qui est requis dans le cas d'une fusion profitable qui accroît le niveau de mieux‐ être, et presque deux fois deux fois plus élevées pour satisfaire la norme des surplus pondérés.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".